We implement our 01 loss network (MLP01) in Python and Pytorch [1]}, the sigmoid activated cross-entropy loss network (MLP) in scikit-learn [2]}, and binarized neural network (BNN) with the Larq software suite https://github.com/larq/larq. We train MLP with stochastic gradient descent that has a batch size of 200, momentum of 0.9, and learning rate of 0.01. For BNN we use the approximate sign activation [3]} that has been shown to give higher test accuracies than other variants and the original straight through estimator [4]}.
